Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sigistry/marketplacenpx agentmods add commands/sigistry/marketplace/test-data-factoryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/sigistry/marketplace/test-data-factory)<a href="https://agentmods.dev/commands/sigistry/marketplace/test-data-factory"><img src="https://agentmods.dev/badge/commands/sigistry/marketplace/test-data-factory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/sigistry/marketplace/test-data-factory"><img src="https://agentmods.dev/badge/commands/sigistry/marketplace/test-data-factory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00012 | $0.04342 |
| Opus 5 | $0.00006 | $0.02171 |
| Sonnet 5 | $0.00002 | $0.00868 |
| Haiku 4.5 | $0.00001 | $0.00434 |
Grade A, and why
test-data-factory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 752 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Data Factory Generation Command
You are tasked with generating comprehensive test data factories that create realistic, consistent test data for your tests. These factories should make it easy to generate valid test instances with sensible defaults while allowing customization.
Step 1: Identify Target Entities
If the user provided entity/model names:
- Locate the corresponding data models, classes, or type definitions
- Identify all properties and their types
- Understand relationships between entities
If no specific entities are provided:
- Scan the codebase for data models
- Suggest common entities: User, Product, Order, etc.
- Show recently modified models
Common entity locations:
- TypeScript:
interfaces,types,models - Python:
models.py,schemas.py, dataclasses - Java: entity classes, DTOs
- Go: structs
Step 2: Analyze Entity Structure
For each entity, extract:
Basic Properties
- Property names
- Data types (string, number, boolean, date, etc.)
- Required vs optional fields
- Default values
- Constraints (min/max, length, format)
Complex Properties
- Nested objects
- Arrays and collections
- Enums and unions
- Relationships (foreign keys, references)
Validation Rules
- Format requirements (email, URL, phone)
- Range constraints (min, max values)
- Length limits
- Regular expression patterns
- Custom validators
Example Analysis:
// Source: User.ts
interface User {
id: string; // UUID
email: string; // Valid email format
firstName: string; // 1-50 chars
lastName: string; // 1-50 chars
age?: number; // Optional, 18-120
role: 'admin' | 'user'; // Enum
createdAt: Date;
settings: UserSettings; // Nested object
orders: Order[]; // Array of related entities
}
Step 3: Detect Faker/Mock Library
Identify available data generation libraries:
JavaScript/TypeScript
- @faker-js/faker (recommended): Comprehensive fake data
- chance: Random data generator
- casual: Fake data for testing
- Install if not present:
npm install -D @faker-js/faker
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 752 lines · 12 tokens per session scan A 6631d27ed94e
test-data-factory is a command published in the GitHub repository sigistry/marketplace (3 stars, last pushed 4d ago), licensed MIT. It adds 12 tokens to every session and 4,342 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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